Output feedback control of an uncertain input-delayed nonlinear system with bounded control commands

This study focused on controlling a class of nonlinear systems with actuation time delays. We proposed a novel output-feedback controller in which the magnitude of the input commands is saturated and can be adjusted by varying control parameters. In this design, a predictor term is used to compensate for delays in the input, and auxiliary systems are exploited to provide a priori bounded control commands and account for the lack of full-state information. The stability analysis results revealed that uniformly ultimately bounded tracking is guaranteed despite modeling uncertainties and additive time-varying disturbances in the system dynamics. The performance of the controller was evaluated through simulation.     

Adaptive dynamic surface tracking control for uncertain full-state constrained nonlinear systems with disturbance compensation

In this paper, an asymptotic adaptive dynamic surface tracking control strategy is investigated for uncertain full-state constrained nonlinear systems subject to parametric uncertainties and external disturbances. A novel disturbance estimator (DE) is firstly used to compensate for external disturbances. The parametric uncertainties are accordingly handled via a synthesized adaptive law. Then, by using the barrier Lyapunov function (BLF) and dynamic surface control (DSC), an appropriate backstepping design framework employing a novel adaptive-gain nonlinear filter is given, which avoids the “explosion of complexity” and relieves the conservatism of filter gain selection. The theoretical analysis reveals the asymptotic tracking performance is assured with the proposed controller. In the end, some simulation cases demonstrate the validity of the proposed controller.     

Robust formation control for uncertain multi-agent systems

Robust formation problems for linear multi-agent systems with uncertainties and external disturbances are investigated in this paper. The model of each agent can be described by a nominal linear system combined with external disturbances and uncertainties which include parameter perturbations and nonlinear uncertainties. A more general bound of uncertainties is introduced. A robust formation controller, which consists of a nominal controller and a robust compensator, is proposed to achieve the desired state formation and restrain the influence of uncertainties and disturbances. Furthermore, sufficient conditions for time-varying formation feasibility are introduced and proved. Finally, a numerical example is provided to demonstrate the theoretical results.     

Robust H∞ persistent dwell time control for switched discrete-time T–S fuzzy systems with uncertainty and time-varying delay

This paper is concerned with the controller synthesis for switched Takagi–Sugeno (T–S) fuzzy systems with time-varying delays, parameter uncertainties and process disturbances. A persistent dwell time (PDT) based control law is mainly proposed for the T–S fuzzy systems in presenting of high-frequency motion switches. Different with the most existing literatures, the dynamics of local subsystems are allowed to be unstable during fast switching time intervals as well as the jump time instants. In addition, the maximal period of persistence time is not limited. Under the influences of the time-varying delays, uncertainties and disturbances, the proposed method ensures the overall closed-loop system to be globally uniformly exponentially stable. Moreover, a pre-given H_∞ performance can be simultaneously guaranteed. Numerical examples are provided to demonstrate the effectiveness of the proposed method.     

Sum-of-squares-based robust H∞ controller design for discrete-time polynomial fuzzy systems

This paper investigates a robust H_∞ controller design for discrete-time polynomial fuzzy systems based on the sum-of-squares (SOS) approach when model uncertainties and external disturbances are simultaneously considered. At the beginning of the controller design procedure, a general discrete-time polynomial fuzzy control system proposed in this paper is used to represent a nonlinear system containing model uncertainties and external disturbances. Subsequently, through use of a nonquadratic Lyapunov function and the H_∞ performance index, the novel SOS-based robust H_∞ stability conditions are derived to guarantee the stability of the entire control system. By solving those stability conditions, control gains of the robust H_∞ polynomial fuzzy controller are obtained. Because the model uncertainties and external disturbances are considered simultaneously in the controller design procedure, the closed-loop control system achieves greater robustness and H_∞ performance against model uncertainties and external disturbances. Moreover, the novel operating-domain-based robust H_∞ stability conditions are derived by considering the operating domain constraint to relax the conservativeness of solving the stability conditions. Finally, simulation results demonstrated the availability and effectiveness of the proposed stability conditions, which are more general than those used in existing approaches.     

Adaptive prescribed performance control of nonlinear asymmetric input saturated systems with application to AUVs

In this paper, the adaptive prescribed performance tracking control of nonlinear asymmetric input saturated systems in strict-feedback form is addressed under the consideration of model uncertainties and external disturbances. A radial basis function neural network (RBF-NN) is utilized to handle the model uncertainties. By prescribed performance functions, the transient performance of the system can be guaranteed. The continuous Gaussian error function is represented as an approximation of asymmetric saturation nonlinearity such that the backstepping technique can be leveraged in the control design. Based on the Lyapunov synthesis, residual function approximation inaccuracies and external disturbances are compensated by constructed adaptive control laws. As a consequence, all the signals in the closed-loop system are uniformly ultimately bounded and the tracking errors bounded by prescribed functions converge to a small neighbourhood of zero. The proposed method is applied to the autonomous underwater vehicles (AUVs) with extensive simulation results demonstrating the effectiveness of the proposed method.     

Design of an adaptive missile autopilot considering the boost phase using the SDRE method and neural networks

An adaptive autopilot to control a skid-to-turn missile during its boost phase is designed using the state-dependent Riccati equation (SDRE) method and neural networks (NN). To address the rapid changes in parameters during the boost phase, the translational and rotational motions of the missile are modeled with time-varying velocity and inertial parameters. The autopilot with a two-loop structure is designed to perform integrated roll-pitch-yaw control of the missile with cross-coupled dynamics; each loop has a baseline controller and an adaptive controller. The baseline controller is designed using the SDRE method for reference command tracking in a nominal environment, and the adaptive controller is designed based on NN to manage uncertainty during the boost phase. Stability analysis of the closed-loop system is performed, and the performance of the proposed autopilot is demonstrated by numerical simulation.     

Fixed-time neuroadaptive practical tracking control based on extended state/disturbance observer for a QUAV with external disturbances and time-varying parameters

This paper investigates the fixed-time neural network adaptive (FNNA) tracking control of a quadrotor unmanned aerial vehicle (QUAV) to achieve flight safety and high efficiency. By combining radial basis function neural network (RBFNN) with fixed time adaptive sliding mode algorithm, a novel radial basis function neural network adaptive law is proposed. In addition, an extended state/disturbance observer (ESDO) is proposed to solve the problem of unmeasurable state and external interference, which can obtain reliable state feedback and interference input. Unlike most other ESO applications, this paper does not set the uncertainty model and external disturbances as total disturbances. Instead, the external disturbances are observed by extending the states and the observed states are fed back to the controller to cancel the disturbances. In view of the time-varying resistance coefficient and inertia torque in the QUAV model, the neural network is introduced so that the controller does not need to consider these nonlinear uncertainties. Finally, a numerical example is given to verify the effectiveness of the coupled non-simplified QUAV model.     

Asymptotic adaptive tracking control and application to mechatronic systems

This article develops an asymptotic tracking control strategy for uncertain nonlinear systems subject to additive disturbances and parametric uncertainties. To fulfill this work, an adaptive-gain disturbance observer (AGDO) is first designed to estimate additive disturbances and compensate them in a feedforward way, which eliminates the impact of additive disturbances on tracking performance. Meanwhile, an updated observer gain law driven by observer estimation errors is adopted in AGDO, which reduces the conservatism of observer gain selection and is beneficial to practical implementation. Also, the parametric uncertainties existing in systems are addressed via an integrated parametric adaptive law, which further decreases the learning burden of AGDO. Based on the parametric adaption technique and the proposed AGDO approach, a composite controller is employed. The stability analysis uncovers the system asymptotic tracking performance can be attained even when facing time-variant additive disturbances and parametric uncertainties. In the end, comparative experimental results of an actual mechatronic system driven by a dc motor uncover the validity of the developed approach.     

Predictor-based optimal robust guaranteed cost control for uncertain nonlinear systems with completely tracking errors constraint

In this paper, a novel composite controller is proposed to achieve the prescribed performance of completely tracking errors for a class of uncertain nonlinear systems. The proposed controller contains a feedforward controller and a feedback controller. The feedforward controller is constructed by incorporating the prescribed performance function (PPF) and a state predictor into the neural dynamic surface approach to guarantee the transient and steady-state responses of completely tracking errors within prescribed boundaries. Different from the traditional adaptive laws which are commonly updated by the system tracking error, the state predictor uses the prediction error to update the neural network (NN) weights such that a smooth and fast approximation for the unknown nonlinearity can be obtained without incurring high-frequency oscillations. Since the uncertainties existing in the system may influence the prescribed performance of tracking error and the estimation accuracy of NN, an optimal robust guaranteed cost control (ORGCC) is designed as the feedback controller to make the closed-loop system robustly stable and further guarantee that the system cost function is not more than a specified upper bound. The stabilities of the whole closed-loop control system is certified by the Lyapunov theory. Simulation and experimental results based on a servomechanism are conducted to demonstrate the effectiveness of the proposed method.     

Robust time-varying output formation tracking for heterogeneous multi-agent systems with adaptive event-triggered mechanism

This paper investigates the time-varying output formation tracking problem of heterogeneous multi-agent systems subjected to model uncertainties and external disturbances via adaptive event-triggered mechanism. Firstly, an adaptive distributed event-triggered observer is constructed to acquire the leader’s state and a time-varying formation output tracking controller utilizing sliding mode method is proposed to deal with the model uncertainties and external disturbances can be addressed. Secondly, an algorithm is given to claim the design procedures of the event-triggered based controller and asymptotic convergence of the controller is proved based on Lyapunov theory. Thirdly, Zeno-behavior is proved to be excluded strictly. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.     

Decentralized low-complexity tracking of uncertain interconnected high-order nonlinear systems with unknown high powers

A global decentralized low-complexity tracker design methodology is proposed for uncertain interconnected high-order nonlinear systems with unknown high powers. It is assumed that interconnected nonlinearities are bounded by completely unknown nonlinearities, rather than, a linear combination of high-ordered state variables. Compared with the existing decentralized results for interconnected nonlinear systems with known high powers, the decentralized robust controller, which achieves the pre-designable transient and steady-state tracking performance for each subsystem, is designed by employing nonlinear error surfaces with time-varying performance functions, regardless of unknown nonlinear interactions and high powers related to virtual and actual control variables. The proposed decentralized continuous robust low-complexity tracker is realized without the use of any adaptive or function approximation techniques for estimating unknown parameters and nonlinearities. The stability and preassigned tracking performance of the resulting decentralized low-complexity control system are thoroughly analyzed in the Lyapunov sense. Finally, simulation results on coupled underactuated mechanical systems are provided to show the effectiveness of the proposed theoretical result.     

A novel general type-2 fuzzy controller for fractional-order multi-agent systems under unknown time-varying topology

In this paper, a robust adaptive control scheme is proposed for the leader following control of a class of fractional-order multi-agent systems (FMAS). The asymptotic stability is shown by a linear matrix inequality (LMI) approach. The nonlinear dynamics of the agents are assumed to be unknown. Moreover, the communication topology among the agents is assumed to be unknown and time-varying. A deep general type-2 fuzzy system (DGT2FS) using restricted Boltzmann machine (RMB) and contrastive divergence (CD) learning algorithm is proposed to estimate uncertainties. The simulation studies presented indicate that the proposed control method results in good performance under time-varying topology, unknown dynamics and external disturbances. The effectiveness of the proposed DGT2FS is verified also on modeling problems with high dimensional real-world data sets.     

Event-triggered adaptive tracking control of uncertain nonlinear systems with prescribed performance and actuator failures

This article studies adaptive prescribed performance tracking control problem for a class of strict-feedback nonlinear systems with parametric uncertainties and actuator failures. Firstly, in order to compensate the multiple uncertainties and eliminate the influence of actuator failure, a new adaptive tracking controller based on first-order filter technology will be proposed, which simplifies the algorithm design process. Then, by introducing an asymmetric state transition function, the transient and steady performances of the output tracking error are both constrained such that the predetermined performance control goal is achieved. Moreover, to reduce the communication burden from the controller to the actuator, the event-triggered mechanism is designed, and there will be no Zeno phenomenon. Based on Lyapunov stability theory, it is strictly proved that output signal can track the reference signal and all the signals of the closed-loop system are bounded. Finally, a simulation example is performed and the results demonstrate effectiveness of the proposed strategy.     

Event-triggered adaptive neural control for state-constrained switched nonlinear systems based on nonlinear shifting function technique

This paper addresses the event-triggered tracking control design for state-constrained switched nonstrict feedback nonlinear systems. With the help of a time-varying nonlinear shifting function (TVNSF) introduced into the switched nonlinear system, the proposed solution is seen as a unified tool regardless of whether the constraint conditions are state constraints, output constraint, or even no constraint. Also, by allowing the triggering error to vary with the switching signal in time, the negative effects of the mismatch between the individual controller and the subsystem on system performance are trumped. Moreover, by using constructed individual Lyapunov function that depends on the lower bound of the control gain function of individual subsystem, a novel switching signal satisfying the average dwell time (ADT) is provided to ensure the boundedness of all variables in the closed-loop system. Finally, the proposed theory is carried over into a mass-spring-damper system to verify its effectiveness.     

Robust cascaded horizontal-plane trajectory tracking for fixed-wing unmanned aerial vehicles

This paper investigates the problem of horizontal-plane trajectory tracking for fixed-wing unmanned aerial vehicles(UAVs) subjected to external disturbances and uncertainties including coupling and unmodeled dynamics. Under the assumption there exist ideal inner-loop controllers, the 12-state model is reduced to a 6-state translational motion model, which is described by a group of simplified nonlinear equations with equivalent disturbances via introducing general aerodynamic models. Then a new cascaded control structure consisting of an outer-loop controller for position control and inner-loop controllers for attitude and thrust control is proposed. Based on feedback linearization technology and signal compensation theory, the proposed controller applied for position control incorporates a nominal linear time-invariant controller and a robust compensator, the latter of which is introduced to restrain the effects of uncertainties and disturbances. The robust performance of the closed-loop system is proved. Actual experimental results conducted on a small fixed-wing aircraft demonstrate that the proposed control approach is effective.     

Observer-based terminal sliding mode control of non-affine nonlinear systems: Finite-time approach

This paper studies the problem of observer based fast nonsingular terminal sliding mode control schemes for nonlinear non-affine systems with actuator faults, unknown states, and external disturbances. A hyperbolic tangent function based extended state observer is considered to estimate unknown states, which enhances robustness by estimating external disturbance. Then, Taylor series expansion is employed for the non-affine nonlinear system with actuator faults, which transforms it to an affine form system to simplify disturbance observer and controller design. A finite time disturbance observer is designed to address unknown compound disturbances, which includes external disturbances and system uncertainties. A fast nonsingular terminal sliding mode with exponential function sliding mode is proposed to address output tracking. Simulation results show the proposed scheme is effective.     

Finite-time guaranteed cost control for uncertain delayed switched nonlinear stochastic systems

This paper studies the finite-time guaranteed cost control problem for switched nonlinear stochastic systems with parameter uncertainties and time-varying delays. By choosing a model-dependent and delay-dependent Lyapunov-Krasovskii functional, applying the average dwell time approach and the Gronwall inequality, some novel sufficient conditions are derived to ensure that the switched nonlinear stochastic closed-loop system is finite-time stochastically stable and an upper bound is given on the performance index. The obtained nonlinear matrix is transformed into a linear matrix form, and then the feedback controller gains of the switched nonlinear stochastic systems with time-varying delay are obtained. Finally, two simulation examples are designed to verify the effectiveness of the suggested approach.     

Anti-disturbance control based on disturbance observer for nonlinear systems with bounded disturbances

A composite anti-disturbance control problem for a class of nonlinear systems is studied in this paper. There are two types of disturbances in the systems, one is the matched disturbance with bounded variation rate, the other is the unmatched time-varying disturbances. A nonlinear disturbance observer is designed to estimate the matched disturbances, which can be presented separately from the controller design. By integrating DOBC with back-stepping method, a composite DOBC and back-stepping controller is proposed, and the disturbance estimations are introduced into the design of virtual control laws to compensate the unmatched disturbances. In addition, it is proved that all the states in the closed-loop system are uniformly ultimate bounded (UUB). Finally, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method.     

Neuroadaptive deferred full-state constraints control without feasibility conditions for uncertain nonlinear EASSs

This article studies the neuroadaptive full-state constraints control problem for a class of electromagnetic active suspension systems (EASSs). First, the original constraint system with arbitrary initial values is transformed into a new constraint system with zero initial values by using the shift function method. Then, a new kind of cotangent-type nonlinear state-dependent transition function is constructed to solve the asymmetric time-varying full-state constraints control problem, which eliminates the limitation that the virtual controller needs to satisfy the feasibility conditions in the previous full-state constraints control based on Barrier Lyapunov Function (BLF) and Integral BLF. Furthermore, the neural networks (NNs) are used as nonlinear function approximators to deal with the unknown nonlinear dynamics of EASSs, a neuroadaptive full-state constraints control design method is proposed under the Backstepping recursive design framework. Finally, the effectiveness of the proposed method is verified by a simulation of EASSs with road disturbances.